京 都 大 学 防 災 研 究 所 年 報 第 50 号 C 平 成 19 年 4 月
Annuals of Disas. Prev. Res. Inst., Kyoto Univ., No. 50 C, 2007

Analysis and Prediction of Anomalous Weather and Atmospheric Hazards

Hiromasa KAWAI, Tatsuya IWASHIMA, Hirohiko ISHIKAWA,
Takashi MARUYAMA, Hitoshi MUKOUGAWA,
Mitsuaki HORIGUCHI, Takao IGUCHI and Tokihiko ARAKI
Synopsis
This paper describes the analysis of anomalous weather and its hazards, which consists ofthree parts. The first part describes the predictability of a downward migration of the negative
Northern Annular Mode (NAM) anomaly following a stratospheric sudden warming (SSW)
occurring in January 2003. The second part describes strong wind disasters caused by typhoon,
tornados and down bursts in 2006. The last part describes wind pattern of the descending
high-speed turbulence structure in the nearly neutral atmospheric boundary layer and the possibilityof tornado prediction with environmental parameter.
Keywords: anomalous weather, predictability, ensemble prediction, northern annular mode,
stratospheric sudden warming, Typhoon 0613, strong wind, damage, turbulence structure,atmospheric boundary layer, tornado, EHI
1.

Predictability of the Downward Migration of theNorthern Annular Mode: A Case Study for
January 200

1.1 Introduction
Recent studies have revealed that the understandingof the downward influence of the stratosphericcirculation change on the troposphere is important toimprove the forecast skill of the extended-range
predictions. In particular, the Northern Annular Mode
(NAM) corresponding to the dominant mode of theextratropical atmosphere is considered to be a key
component to understand such downward influence ofthe stratosphere due to its downward migration
characteristics revealed by Baldwin and Dunkerton(1999, 2001). The NAM is characterized by thevariation in the strength of the zonal-mean zonal flow
along 55N, and related to the occurrence of theanomalous weather condition. Baldwin et al. (2003)
proposed a statistical method to predict themonthly-mean Arctic Oscillation (AO) signature in thelower atmosphere using the preceding NAM index in
the lower stratosphere. The AO signature is identical to

the NAM index at 1000 hPa. Since the variation of thepredicted AO signature based on the preceding NAM
index at 150 hPa is larger than that based on thepreceding AO signature, they insisted the validity of the
proposed statistical method to predict the AO signature.
However, the practical limit of the predictability of the
AO signature associated with the downward migrationof the NAM has not been revealed using operational
extended-range numerical prediction results.
On the other hand, our recent works [Mukougawa
and Hirooka, 2004 (hereafter M04); Mukougawa et al.,2005 (hereafter M05)] indicated the prolonged
predictability of stratospheric circulation during
stratospheric sudden warming (SSW) events up to 1
month using the Japan Meteorological Agency (JMA)
1-month prediction results. The prolonged
predictability is associated with the long time-scale
inherent to the planetary waves propagating upwardfrom the troposphere before the SSW events. Moreover,since the stratospheric circulation during the SSW
events is characterized by negatively large NAM
anomalies which descend gradually, we could expect
prolonged predictability of negative AO events after－ 77 －

associated with the first EOF mode.

SSW episodes. Thus, in this study, we will examine
the dynamics and the predictability of downward
migration of negative NAM anomalies observed in
January 2003 just after a SSW event using all membersof the JMA 1-month ensemble prediction results. We
will also try to reveal the precursory event for the
downward migration of the negative NAM anomaly tothe troposphere.
1.2 Data and Analysis Method
We used operational ensemble 1-month (34-day)forecast data sets provided by the JMA from November
2003 to January 2004. The JMA ensemble 1-monthprediction has been carried out at 1200UTC every
Wednesday and Thursday with 12 perturbed and 1
unperturbed initial conditions. Numerical integrations
are conducted using a JMA global spectral model(JMA-GSM0103) with triangular 106 truncation(T106) and 40 vertical levels up to 0.4 hPa. For further
model details, the reader should refer to M05. The
initial perturbations are obtained using the Breeding ofGrowing Modes (BGM) method (Toth and Kalnay,1993). The forecast data have been archived every 24
h for the 34-day prediction period on a 2.5-degree
longitude-latitude grid at 22 levels from 1000 to 1 hPa.
To verify model forecasts, JMA Global Analyses data
set with 1.25-degree horizontal resolution at 23 levels
from 1000 to 0.4 hPa is used.
As in M04, we also used the operational ECMWF
twice-daily data set during 1985--2001 with 2.5-degree
horizontal resolution at 14 levels from 1000 to 10 hPa to
obtain the NAM index by the following procedure. First,
the seasonal cycle, averaged over 17 years and subject to5-day running average, is removed from the daily
geopotential height on each gridpoint to define heightanomalies. After applying a 10-day low-pass filter(Blackmon, 1976) to the anomalies, we perform a
combined EOF analysis which combines geopotential
height field at 50, 500, and 1000 hPa levels as in
Yamazaki and Shinya (1999) to the low-pass filtered
wintertime (November-April) anomalies. The analysis
domain is poleward of 20N, and the grid data areweighted by the square root of the cosine of latitude as
well as the square root of density at each level. The
leading combined EOF mode corresponding to the NAM
accounts for 11.3% of the total variance. Finally, theNAM index at each level is defined as the projection of
the daily anomaly field onto the regression map

1.3 Results
During January 2003, a warming episode took
place in the Northern Hemisphere stratosphere (Fig. 1),
corresponding to a negatively large NAM event in thestratosphere. The SSW was caused by the amplificationof zonal wavenumber (WN) 1 components. After the
SSW, the negative NAM anomaly migrates downward
to the surface from 17 through 21 January. In order toanalyze the dynamics of the downward migration, thezonal-mean zonal wind acceleration averaged over
50N-70N is also shown in the bottom panel of Fig. 1.
The deceleration at 10 hPa around 15 January is
associated with the SSW due to the amplified WN 1
component. The deceleration extends down to thelower troposphere accompanied with the downward
migration of the NAM index from 17 to 20 January.
The gradual downward migration of the deceleration is
confined within the stratosphere whereas the zonal
wind concurrently decelerates over the entire
troposphere around 18 January.

Fig.1. Time-height [pressure (hPa)] cross section of theobserved NAM index in January 2003 (upper panel),and the observed zonal-mean zonal wind acceleration(m/s/day) averaged over 50N-70N. Shaded lightly(heavily) are where values are smaller than -1 (-2).
In order to examine the predictability of the SSW
by the JMA 1-month ensemble forecast, the time
variation of observed and predicted zonal-meantemperature at 80N and 10hPa during January 2003 is－ 78 －

shown in Fig. 2. For the forecasts starting from 1 and 2
January, all the ensemble members failed in predicting
the occurrence of the SSW whereas those from 8 and 9
January made successful predictions. Therefore, theSSW event is predictable more than 10 days in advance.
This predictable period is somewhat shorter than that of
the SSW in December 2001 reported by M05. It should
be noted that the spread between the members startingfrom 15 and 16 January (Fig. 2c) is very small
compared with those in Figs. 2a and 2b.

Fig.2. Time variation of the zonal-mean temperature(K) at 80N and 10hPa in January 2003 for theobservation (thick solid lines) and the JMA ensemble
forecasts (thin solid lines) starting from 1 and 2 January(a), 8 and 9 January (b), and 15 and 16 January 2003(c). The dotted and broken lines in (b) coincide with
run A and run B in Fig. 3, respectively. As regards run
A and run B, see the text.
On the other hand, Fig. 3 shows the observed andthe predicted NAM index at 1000 hPa (the AO
signature). In the following, we will focus on thenegative AO event around 21 January just after the
SSW. For the forecasts starting from 1 and 2 January,which failed in predicting the SSW, the spread of thepredicted AO signature around 21 January is too large
to discuss the predictability of the negative AO event(Fig. 3a). The ensemble members starting from 8 and 9
January, which succeeded in predicting the SSW, also
have a large spread for the AO prediction (Fig. 3b). In
fact, some members predict a positive AO signature
just after the SSW event. Moreover, although all themembers starting from one week after (Fig. 3c) predictnegative AO signatures around 21 January, the spreadof the predicted AO signature is still large. Thus, it isfound that the predictable period of the negative AO
event is at most 6 days, which is much shorter than thatof the precedent SSW event.


Fig.3. As in Fig. 2, but for AO signature.
In order to reveal the influence of the stratosphericcirculation to the prediction of the negative AO event
after the SSW, we will examine the ensemble
prediction starting from 8 and 9 January in detail (Fig.
3b). At first, we will compare the best (run A; thedotted line) and the worst forecast (run B; the brokenline) for the prediction of the negative AO event among
the ensemble members. The predicted AO signature by
run A (run B) attains the minimum (maximum) value
on 21 January among the ensemble members. The－ 79 －

Fig.4. Latitude-height cross sections for the time-mean WN 2 E-P flux (arrows; kg/s2) and the associated zonal force
per unit mass (contours; m/s/day) averaged over 18-20 January 2003. (a) The observation, (b) run A, and (c) run B.
Arrows in the right bottom correspond to (4.0 u 108, 1.5 u 106). Shaded lightly (heavily) are where values are smaller
than -5 (-10) m/s/day.
analysis based on the transformed Eulerian meanequation reveals that the E-P flux convergenceassociated with WN 2 components plays a dominantrole in the zonal wind deceleration poleward of 50N in
the troposphere as well as in the stratosphere after the
SSW (not shown).
Then, we investigated the characteristic of WN 2planetary waves over a 3-day period from 18 to 20
January, when the zonal wind deceleration becomes
evident. Figure 4 shows the E-P flux vector and theassociated convergence averaged over the 3-day period.
For the observation (Fig. 4a), the upward propagationof WN 2 components concentrates around 50N in the
troposphere, and there is strong convergence of the E-P
flux in the poleward region of the upper troposphere.
The wave activity further propagates upward andpoleward in the stratosphere, and converges in the
middle stratosphere. Run A (Fig. 4b) has a somewhat
weaker wave activity than the observation, and the
upward propagating region shifts poleward. The
convergence of the E-P flux in the upper troposphere is
still evident as in the observation. However, for run B(Fig. 4c), the convergence of the E-P flux is
considerably weak, and the wave activity tends topropagate equatorward in the upper troposphere, incontradiction to the observation and run A. Therefore, itis necessary to well simulate the behavior of WN 2planetary waves in the upper troposphere for theprediction of the downward migration of the negative
NAM anomaly into the troposphere. Figure 4a also
reveals that anomalously enhanced E-P flux

convergence occurs in two discrete regions around the
50-hPa level and the tropopausal region. This suggeststhat the framework of the gradual downward migrationof the negative NAM index from the stratosphere to thelower troposphere is not appropriate for this negative
NAM event. Rather, the zonal winds in the troposphere
decelerate concurrently with those in the stratosphere.
The propagating property of WN 2 planetary waves
during this period is much influenced by thezonal-mean zonal wind distribution during (not shown).
In particular, by examining the corresponding refractive
index squared for stationary WN 2 component(Andrews et al., 1987), it is found that the slightdifference in the zonal-mean zonal wind profile in the
tropopausal region around 60N significantly affects the
propagating property of the WN 2 component in theupper troposphere. On the contrary, the difference ofthe refractive index squared in the lower stratosphere isnot so evident. The evanescent region with negativerefractive index squared prevails in the midlatitudes forthe observation and run A as well as run B. The
possible relationship between the characteristic
zonal-mean zonal wind profile and the subsequent
occurrence of the negative AO event is also confirmedby the regression analysis on zonal wind using all
ensemble (26) members of the forecast starting from 8
and 9 January as in M05.
1.4

Concluding Remarks]
We examined the dynamics and the predictability ofa downward migration event of the negative NAM－ 80 －

anomaly occurring in January 2003 just after a SSW by
the use of all ensemble members of the 1-monthforecasts performed by the JMA. It was found that theE-P flux convergence associated with the WN 2
component plays a dominant role for the downward
migration of the negative NAM anomaly while the
SSW is caused by the amplification of the WN 1
component. The predictable period of the negative
NAM anomaly in the troposphere is 6 days at most,and the ensemble members succeeded in predicting theoccurrence of the SSW does not necessarily well
predict the following negative NAM anomaly in the
troposphere. Thus, the predictability of the negative
NAM anomaly in the troposphere is quite limited
compared with that of the SSW.
It is also revealed that the fair reproduction of WN
2 planetary waves propagating upward and poleward
from the troposphere into the stratosphere is important
for the successful prediction of the downward
migration of the negative NAM anomaly. The analysis
on the refractive index indicates that the tropopausal
zonal-mean zonal wind profile rather than thestratospheric zonal-mean zonal wind anomaly after the
SSW significantly affects the propagating property of
the WN 2 component. Hence, it is suggested that thestratospheric zonal wind anomaly after the SSW canhardly affect the predictability of the tropospheric
negative NAM anomaly in comparison with thetropopausal zonal wind anomaly.
Of course, we have to examine other downward
migration events of the negative NAM anomaly toconfirm our results. Moreover, hindcast experiments
using the numerical prediction model is also necessary
to reveal the dynamical relationship between the
tropopausal zonal wind anomaly and the propagationand the generation of the WN 2 planetary waves during
this period.
1.5

Acknowledgments
We would like to thank all the members in
Numerical Prediction and Climate Prediction Divisions
in JMA for providing us 1-month forecast data sets of
JMA. The GFD-DENNOU Library was used for thegraphics.

2.

Characteristics of damage to buildings andhouses caused by strong wind of Typhoon 0613

2.1

Location and Meteorological records
Typhoon 0613 was born on the 10th of
September in 2006 and began its approach to
Yaeyama Islands on September 15. After passing
near by Ishigaki Island, the main island of Yaeyama
Islands, it landed by Kyushu, the western to mainisland of Japan as shown in Fig.5. The maximum
peak gust of 67.0m/s has been recorded at 07:19
September 16 at Ishigaki city, which is the second
strong wind ever measured at weather station in
Ishigaki Island. The typhoon brought a number ofstrong gusts and tornados during the approach to
Kyusyu.6here were a strong gust and fore tornados
in Miyazaki prefecture, two strong gusts in Ooita
prefecture and a tornado in Kochi prefecture, whichcaused damage to buildings and houses. The
typhoon also brought strong wind disasters in the
Northeast part of Kyusyu as shown in Fig.6.
20

19
18
16/09JST
16/06

17

16
15
14 13

⷏⴫ፉ

⍹၂ፉ
Ishigaki Island

12
11
10 Sep.

Fig.5 Location of Ishigaki Island and the path of
Typhoon 0613.
2.2

Overview of damage
We focus on the strong wind damage in Ishigaki
Island and Nobeoka city in this report. The
characteristics of damage to buildings and houses were
examined.
(1) Ishigaki Island
According to the damage reports later published by
the Okinawa Prefectural Government (October, 2006),
there were 55 people injured in total, including 4 serious－ 81 －

strong gusts or
tornados

Fig.6 Distribution of damage to buildings andhouses in Kyusyu, and locations of strong gusts andtornados.
injuries. A total of 316 buildings and houses weredamaged, including 22 houses that were completely
destroyed, 50 houses that were badly damaged, 244
houses that were partially damaged. About 4.2 thousandpeople live in Ishigaki Island with the total number ofhouseholds at about 2.2 thousand, meaning that 1% of its
residents and 14% of its buildings suffered, respectively.
The damage was mainly due to strong winds and the costof damage was estimated to be about 360 million yen(roughly 3 million US$).
(2) Nobeoka city
According to the damage report published by theNobeoka city (October, 2006), there were 146 people
injured in total, including 3 deaths and 3 serious injuries.
A total of 1,180 buildings and houses were damaged,
including 79 houses that were completely destroyed, 348
houses that were badly damaged, 753 houses that werepartially damaged. About 135 thousand people live in
Nobeoka city with the total number of households at
about 52 thousand, meaning that 0.1% of its residents
and 2.3% of its buildings suffered, respectively. The
damage was mainly due to strong winds caused bytornado and the cost of damage was estimated to beabout 1.3 million yen (roughly 11 thousand US$).
2.3

Detail of damage and discussion

Various kinds of damage were caused from thestrong winds. Numbers of the damage to roofs, walls andopenings has been observed. The roof tiles were blown
off (photo 1a). This proceeds to break the roof (photo 1b).
Some of less-maintained or old houses were collapsed(photo 1c). Metal roof panels were torn off (photo 2).
The ceiling panels under eaves and the walls fell down
(photo 3,4). Heavy or huge object such as water tankfrom the roof was observed (photo 5). Those objects
sometimes flew and hit other buildings and houses
downwind and damaged to walls or openings (photo 6,7)
or cut the electric lines. Every thing might come fromcollapsed houses in case of very strong winds by tornado(photo 8). The break of opening caused to damage tointerior (photo 9,10). The installations indispensable torecovering from the disaster, such as hospital, police
office, city office or firehouse and so on, should behighly prevented to the flying missiles. The shutters(photo 11), partitioned sash with small area and thickglass (photo 12) or wind resistant glasses (laminated,tempered, filmed or wired glass) will be effective.
Remarkable damage caused by lightweight houses.
Number of steel storehouses or prefabricated houses
collapsed (photo 13), got upon the neighboring house(photo 14, 15), blocked the road (photo 16) or broke thetelephone poles (photo 17). Usually the lightweighthouse is simply fastened to the foundations by ironclamps or just set on the ground with small anchors. Thiscan easily blow off (photo 18) or overturn them. Thosekinds of disaster could be often occurred in the urban
area. Therefore they should be wind resistant designed
not to be blown off or be overturned. The damage totelegraph poles is also serious. Cut of electricity is athreat to our daily life. The broken poles block the roadand obstruct the traffic and transportation (photo 19).
2.4

Conclusion
This paper reports the results of a survey of strong
wind damage caused by Typhoon 0613 in 2006. We
examine the characteristics of recent damage to buildings
and houses.
To prevent the claddings and openings from theflying missiles is important to reduce the strong winddamage. The shutters and the wind resistant glasses are
effective. To decrease the number of missile is alsoessential. The damage of lightweight houses is notable inurban area.

－ 82 －

a. Tearing off of corner roof tiles
b. Damaged roofc. Collapsed one
Photo 1 Strong wind damage to traditional wooden houses

Photo 2 Torn-off roof tiles
on gymnasium

Photo 3 Fall down ofceiling panel

Photo 4
Fall down of wall

Photo 5
Fall down of water tank

Photo 6 Damage by missileof tornado

Photo 7 Hit bytorn-off metal roof

Photo 8 Stack offlying debris by tornado

Photo 9 Interior damage
by wind and rain

Photo 10 Broken
windows of firehouse

Photo 11 Openings
prevented by shutter

Photo 12 Openings installed Photo13 Collapsed
partitioned sash with thick glass
storehouse steel construction

Photo14 Lightweight
Photo15 Lightweight steel
prefabricated house got
storehouse got upon
upon the neighbouring housethe neighbouring house

Photo18 Remained wooden
foundations of a lightweightprefabricated house

Photo19 Telegraph poles
blocked the road－ 83 －

Photo16 Lightweight
Photo17 Lightweightprefabricated houseprefabricated houseblocked the roadbroke a telegraph pole

3. Gusty Turbulence Structure in the Atmospheric
Boundary Layer
2.5

Outline of Observations
Occurrence of disasters by the severe local storms isfrequently related to the descending high-speed
turbulence in the atmospheric boundary layer.
In order to study the high-speed turbulence structurein the nearly neutral atmospheric boundary layer,
observations were carried out at the Shionomisaki Wind
Effect Laboratory of Disaster Prevention ResearchInstitute in 1998 and at the Shigaraki MU Observatory of
Research Institute for Sustainable Humanosphere, Kyoto
University in 2001-2002. For the observation of windprofile in the atmospheric boundary layer, a Doppler
sodar was used. From these observations, we selected
some cases in which the stability of the boundary layer
seems to be nearly neutral.

2.6

Wind Pattern of the High-speed Turbulence
Structure
To begin with, we show the typical case observed atShionomisaki during 1110-1448 LST on 8 Dec. 1998.
In order to detect the high-speed wind events withlarge-scale structures, we analyze the surface layer
turbulence measured by the sonic anemometer (20 m inheight) using the integral wavelet transform. In this study,
we use the “Mexican hat” wavelet function with a fairly
long time scale of 160 s.
w' /ǻw

2.7

2350

u' / ǻu
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
-1.2
-1.4
-1.6

Height (m)

300
250
200
150
100
50
160 120

80

40

0

-40

We pick up the high-speed wind events from thelocal maximum of the wavelet coefficients, which ishigher than a threshold value. Then, a conditionallyaveraged wind pattern on a time-height cross section
from detected six events is constructed using the data
observed by the Doppler sodar (Fig.7). For the averaging,fluctuating components of the streamwise (u’) andvertical (w’) velocity components, which are normalizedusing their variances, are used. In the figure, the contours
depict the normalized u’ values and the arrows show thenormalized w’ values.
This figure shows a descending high-speed
turbulence structure, the central part (maximum u’ value)of which is located at the height near 200 m and at thetime near 30 s before the time of the detected events.
Before the high-speed structure, a low-speed turbulence
structure, which is slightly ascending at the time near 140s before the event time, is also represented.
This pattern is similar to the previously presented
structures by the observation (Gao et al., 1989). They
showed the microfrontal structures for the scalar
concentration and the ejection-sweep structures for thevelocities. In our case, structures could be investigatedover a wide range of height using the Doppler sodar.
Similar wind patterns, which show high-speed
turbulence structures, are depicted in other observation
cases. Therefore, it is certain that turbulence structure
depicted in Fig. N1 appears commonly in the nearly
neutral atmospheric boundary layer.

-80 -120 -160

Time (s)Fig.7 Conditionally averaged wind pattern constructedfrom the data observed by the Doppler sodar on 8 Dec.
1998 at Shionomisaki

Research on the Environmental Condition
Concerning to a Tornadic Outbreak in
Hokkaido
On November 7 at about 13:20 a severe tornado(Tatsumaki) hit Saroma, Hokkaido. In this tornado
outbreak, 47 houses (including residential houses andnon-residential houses) were completely destroyed and
57 were half or partly destroyed. What is worse were 9
persons were killed and 26 persons were injured. The
photo 20 shows a major damaged area in Saroma town.
It is known that development of severe storm and theassociated tornado outbreaks in the United States relateon the atmospheric static stability and the vertical windsheer. Typical parameters used in the assessment of
tornado risk are Energy Helicity Index (EHI) expressedasEHI－ 84 －

CAPE u SREH
1.6 u 10 5

Photo 20 Damage of town caused by the tornado (Photois taken by Shin Corp.)
The EHI is the multiple of CAPE (Convective Available
Potential Energy),

CAPE

g³

ZE
LFC

T ( z)  T ( z)dz
T ( z)and SREH is the Storm Relative Environmental Helicity,

SREH

³

wV ( z ) º .
ªdzk  «( V ( z )  c ) u
wz »¼
¬

3 km
0 km

The CAPE represents the environmental potential for thedevelopment of deep convection and the SREH
represents the potential for vortex generation. When andwhere both CAPE and SRRH have high value, severe
storm accompanying tornado is favorable to occur. The
typical values of these parameters for the tornado
outbreak in United States are shown in Table 1 according
to Bluestein and Jain (1985) and Rasmussen and
Blanchard (1998).
If the EHI is to be used as an indicator of tornado
outbreak even in Japan, there will be possibility to
predict tornado outbreaks in Japan. Thus, values of theseparameters are computed using the Grid Point Value dataof operational numerical prediction conducted by Japan
Meteorological Agency.
The JMA_MSM runs 8 times a day initialized at 00,
03, 06, 09, 12, 15, 18 and 21JST.In this study 4 run
sinitialized at 03, 06, 09, 12JST on Nov. 7 are used.
Using the 3-hourly output of prediction the distributionof EHI is computed for 12 and 15 JST as shown in Fig.8
In the figure, for example, the left-top panel shows the
EHI at 12 JST computed from prediction initialized at
03JST and the right-bottom panel shows that at 15JST
initialized at 12JST. In the figure places of Saroma Town
and other wind hazard are also shown.
It is seen that the EHI computed from the prediction

initialized at 09JST well covers major wind hazards,
tornado at Hidaka-cho, High wind at Rikubetsu-cho andtornado at Saroma-cho. In 12JST distribution, a high EHI
area is seen near Hidaka-cho, and at 15JST a band ofhigh value (EHI>0.2) extend from south which covers
Saroma-cho. The Value is 0.55 near Saroma-cho, whichis comparable to the EHI value for weak tornadoes inUnited States in Table 1. The predictions initialized at 03
and 06 JST yield higher value of EHI but the distribution
is very wide, so that it is difficult to judge the local
potential for tornado outbreak from these results.
Although EHI distribution from prediction initialized
at 09JST shows good agreement with tornado outbreaks,
other high EHI areas are found near Kushiro andShiretoko areas where no wind hazards took place.
Further, it seems that the EHI value seem to increase
generally with the increase of prediction time. Before
using EHI as an indicator of potential for tornado
outbreaks, further studies are necessary. One ofdifficulties for this study is that the number of tornado
outbreak is rather small to perform statistical evaluation
for the performance of EHI. We have to collect tornado
cases as many as possible.
Table 1 The median of CAPE, SREH and EHI for
investigated cases of tornado outbreak in United StateCAPE

F2 d tornadoes
1314

F0 and F1 tonadoes
1152

SREH

180

124

EHI

1.48

0.64

－ 85 －

Initial

EHI at 12JST

EHI at 15JST
+ 12㨔

+ 㧥㨔

03JST

+ 㧥㨔

+ 㧢㨔

B‫ڎ‬

B‫ڎ‬
C‫ڎ‬

06JST

C‫ڎ‬
A‫ڎ‬

A‫ڎ‬

+ 㧢㨔

+ 㧟㨔

B‫ڎ‬
C‫ڎ‬

09JST

B‫ڎ‬
C‫ڎ‬

A‫ڎ‬

A‫ڎ‬

+ 0 h

+ 㧟㨔

12JST

Fig.8 The distribution of EHI at 12JST and 15 JST computed MSM prediction initialized at 03, 06, 09 and
12JST. The A, B and C in the figures denote the places of tornado at Hidaka-cho (11:40), tornado at
Saroma-cho (13:20)‫ޔ‬and wind gust at Rikubetu/Asyoro area (14:30).

－ 86 －

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